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  <div class="section" id="numpy-nanmean">
<h1>numpy.nanmean<a class="headerlink" href="#numpy-nanmean" title="Permalink to this headline">¶</a></h1>
<dl class="function">
<dt id="numpy.nanmean">
<code class="sig-prename descclassname">numpy.</code><code class="sig-name descname">nanmean</code><span class="sig-paren">(</span><em class="sig-param">a</em>, <em class="sig-param">axis=None</em>, <em class="sig-param">dtype=None</em>, <em class="sig-param">out=None</em>, <em class="sig-param">keepdims=&lt;no value&gt;</em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/numpy/numpy/blob/v1.18.1/numpy/lib/nanfunctions.py#L864-L959"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#numpy.nanmean" title="Permalink to this definition">¶</a></dt>
<dd><p>Compute the arithmetic mean along the specified axis, ignoring NaNs.</p>
<p>Returns the average of the array elements.  The average is taken over
the flattened array by default, otherwise over the specified axis.
<code class="xref py py-obj docutils literal notranslate"><span class="pre">float64</span></code> intermediate and return values are used for integer inputs.</p>
<p>For all-NaN slices, NaN is returned and a <em class="xref py py-obj">RuntimeWarning</em> is raised.</p>
<div class="versionadded">
<p><span class="versionmodified added">New in version 1.8.0.</span></p>
</div>
<dl class="field-list">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><dl>
<dt><strong>a</strong><span class="classifier">array_like</span></dt><dd><p>Array containing numbers whose mean is desired. If <em class="xref py py-obj">a</em> is not an
array, a conversion is attempted.</p>
</dd>
<dt><strong>axis</strong><span class="classifier">{int, tuple of int, None}, optional</span></dt><dd><p>Axis or axes along which the means are computed. The default is to compute
the mean of the flattened array.</p>
</dd>
<dt><strong>dtype</strong><span class="classifier">data-type, optional</span></dt><dd><p>Type to use in computing the mean.  For integer inputs, the default
is <code class="xref py py-obj docutils literal notranslate"><span class="pre">float64</span></code>; for inexact inputs, it is the same as the input
dtype.</p>
</dd>
<dt><strong>out</strong><span class="classifier">ndarray, optional</span></dt><dd><p>Alternate output array in which to place the result.  The default
is <code class="docutils literal notranslate"><span class="pre">None</span></code>; if provided, it must have the same shape as the
expected output, but the type will be cast if necessary. See
<em class="xref py py-obj">ufuncs-output-type</em> for more details.</p>
</dd>
<dt><strong>keepdims</strong><span class="classifier">bool, optional</span></dt><dd><p>If this is set to True, the axes which are reduced are left
in the result as dimensions with size one. With this option,
the result will broadcast correctly against the original <em class="xref py py-obj">a</em>.</p>
<p>If the value is anything but the default, then
<em class="xref py py-obj">keepdims</em> will be passed through to the <a class="reference internal" href="numpy.mean.html#numpy.mean" title="numpy.mean"><code class="xref py py-obj docutils literal notranslate"><span class="pre">mean</span></code></a> or <a class="reference internal" href="numpy.sum.html#numpy.sum" title="numpy.sum"><code class="xref py py-obj docutils literal notranslate"><span class="pre">sum</span></code></a> methods
of sub-classes of <a class="reference internal" href="numpy.ndarray.html#numpy.ndarray" title="numpy.ndarray"><code class="xref py py-obj docutils literal notranslate"><span class="pre">ndarray</span></code></a>.  If the sub-classes methods
does not implement <em class="xref py py-obj">keepdims</em> any exceptions will be raised.</p>
</dd>
</dl>
</dd>
<dt class="field-even">Returns</dt>
<dd class="field-even"><dl class="simple">
<dt><strong>m</strong><span class="classifier">ndarray, see dtype parameter above</span></dt><dd><p>If <em class="xref py py-obj">out=None</em>, returns a new array containing the mean values,
otherwise a reference to the output array is returned. Nan is
returned for slices that contain only NaNs.</p>
</dd>
</dl>
</dd>
</dl>
<div class="admonition seealso">
<p class="admonition-title">See also</p>
<dl class="simple">
<dt><a class="reference internal" href="numpy.average.html#numpy.average" title="numpy.average"><code class="xref py py-obj docutils literal notranslate"><span class="pre">average</span></code></a></dt><dd><p>Weighted average</p>
</dd>
<dt><a class="reference internal" href="numpy.mean.html#numpy.mean" title="numpy.mean"><code class="xref py py-obj docutils literal notranslate"><span class="pre">mean</span></code></a></dt><dd><p>Arithmetic mean taken while not ignoring NaNs</p>
</dd>
</dl>
<p><a class="reference internal" href="numpy.var.html#numpy.var" title="numpy.var"><code class="xref py py-obj docutils literal notranslate"><span class="pre">var</span></code></a>, <a class="reference internal" href="numpy.nanvar.html#numpy.nanvar" title="numpy.nanvar"><code class="xref py py-obj docutils literal notranslate"><span class="pre">nanvar</span></code></a></p>
</div>
<p class="rubric">Notes</p>
<p>The arithmetic mean is the sum of the non-NaN elements along the axis
divided by the number of non-NaN elements.</p>
<p>Note that for floating-point input, the mean is computed using the same
precision the input has.  Depending on the input data, this can cause
the results to be inaccurate, especially for <code class="xref py py-obj docutils literal notranslate"><span class="pre">float32</span></code>.  Specifying a
higher-precision accumulator using the <a class="reference internal" href="numpy.dtype.html#numpy.dtype" title="numpy.dtype"><code class="xref py py-obj docutils literal notranslate"><span class="pre">dtype</span></code></a> keyword can alleviate
this issue.</p>
<p class="rubric">Examples</p>
<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="n">a</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">([[</span><span class="mi">1</span><span class="p">,</span> <span class="n">np</span><span class="o">.</span><span class="n">nan</span><span class="p">],</span> <span class="p">[</span><span class="mi">3</span><span class="p">,</span> <span class="mi">4</span><span class="p">]])</span>
<span class="gp">&gt;&gt;&gt; </span><span class="n">np</span><span class="o">.</span><span class="n">nanmean</span><span class="p">(</span><span class="n">a</span><span class="p">)</span>
<span class="go">2.6666666666666665</span>
<span class="gp">&gt;&gt;&gt; </span><span class="n">np</span><span class="o">.</span><span class="n">nanmean</span><span class="p">(</span><span class="n">a</span><span class="p">,</span> <span class="n">axis</span><span class="o">=</span><span class="mi">0</span><span class="p">)</span>
<span class="go">array([2.,  4.])</span>
<span class="gp">&gt;&gt;&gt; </span><span class="n">np</span><span class="o">.</span><span class="n">nanmean</span><span class="p">(</span><span class="n">a</span><span class="p">,</span> <span class="n">axis</span><span class="o">=</span><span class="mi">1</span><span class="p">)</span>
<span class="go">array([1.,  3.5]) # may vary</span>
</pre></div>
</div>
</dd></dl>

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